{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from landlab import RasterModelGrid, imshow_grid\n",
    "from landlab.components import TidalFlowCalculator\n",
    "from landlab.grid.mappers import map_mean_of_link_nodes_to_link,  map_link_vector_components_to_node\n",
    "from landlab.io import read_esri_ascii\n",
    "import tidal_erosion_calculator as tec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre tfc:\n",
      "['topographic__elevation', 'vegetation']\n",
      "['veg_atlink']\n",
      "['topographic__elevation', 'vegetation', 'mean_water__depth']\n"
     ]
    }
   ],
   "source": [
    "# Set parameters (these are from the MarshMorpho2D source code)\n",
    "tidal_period = 12.5 * 3600.0  # tidal period in seconds\n",
    "tidal_range = 3.1  # tidal range in meters\n",
    "roughness_w = 0.02  # Manning's n water\n",
    "roughness_v = 0.1 #manning's n for veg\n",
    "mean_sea_level = 0.0  # mean sea level in meters\n",
    "mwd = 0.01  # minimum depth for water on areas higher than low tide water surface, meters\n",
    "nodata_code = 999  # code for a DEM cell with no valid data\n",
    "tcrgradeint = 0.2; # linear increase in tcr below MLW [pa/m\n",
    "# Read the DEM to create a grid and topography field\n",
    "(grid, z) = read_esri_ascii('zSW3.asc', name='topographic__elevation')\n",
    "\n",
    "#need to create vegetation grid #change to veg at cells\n",
    "veg = grid.add_zeros('vegetation',at='node');\n",
    "veg[z<0] = 1;\n",
    "veg_atlink = grid.map_max_of_link_nodes_to_link('vegetation')\n",
    "grid.add_field('veg_atlink',veg_atlink,at='link')\n",
    "\n",
    "# Configure boundaries: any nodata nodes, plus any nodes higher than mean high tide\n",
    "grid.status_at_node[z==nodata_code] = grid.BC_NODE_IS_CLOSED\n",
    "grid.status_at_node[z>1.8] = grid.BC_NODE_IS_CLOSED\n",
    "boundaries_above_msl = np.logical_and(grid.status_at_node==grid.BC_NODE_IS_FIXED_VALUE, z > 0.0)\n",
    "grid.status_at_node[boundaries_above_msl] = grid.BC_NODE_IS_CLOSED\n",
    "\n",
    "print('pre tfc:')\n",
    "print(grid.at_node.keys())\n",
    "print(grid.at_link.keys())\n",
    "\n",
    "#variable rougness as field in grid\n",
    "roughness_at_nodes = roughness_w + np.zeros(z.size)\n",
    "roughness_at_nodes[z < 0.0] = roughness_v #or roughness_at_nodes[veg] = roughness_v\n",
    "roughness = grid.add_zeros('roughness', at='link')\n",
    "map_mean_of_link_nodes_to_link(grid, roughness_at_nodes, out=roughness)\n",
    "\n",
    "# Instantiate a TidalFlowCalculator component\n",
    "tfc = TidalFlowCalculator(\n",
    "        grid,\n",
    "        tidal_period=tidal_period,\n",
    "        tidal_range=tidal_range,\n",
    "        roughness='roughness',\n",
    "        mean_sea_level=mean_sea_level,\n",
    "        min_water_depth=mwd,\n",
    ")\n",
    "tfc.run_one_step()\n",
    "print(grid.at_node.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(grid.at_node.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tau_cr = 0.2 #Critical stress for unvegetated areas\n",
    "tau_crv = 0.5  #Critical stress for vegetated areas\n",
    "mud_erodability = (10**-5)*24*3600;  # mud erodability kg/m2/s\n",
    "\n",
    "#should this be in seconds, days? he had it in days *24*3600\n",
    "\n",
    "v = grid.at_link['veg_atlink']\n",
    "tec.populateGrids(grid,tfc,tau_cr,tau_crv,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = grid.at_link['mean_water__depth']\n",
    "print(dd.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tec.plot_tidal_flow(grid)\n",
    "print((grid.has_field('cell','hydroperiod_cell')))\n",
    "aa = grid.at_cell['hydroperiod_cell']\n",
    "aa[1] = 1.5234\n",
    "grid.add_field('hydroperiod_cell',aa,at='cell',clobber=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ero = tec.totalsedimenterosion_mudsine(grid, mud_erodability, tidal_range, tcrgradeint)\n",
    "#ok so for some reason erosion value is SUPER high -- no clue why...need to clear that up!\n",
    "print(ero)\n",
    "print(ero.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0824763254786\n",
      "2.05929500354\n",
      "26.4083062745\n",
      "0.1\n",
      "2.15186705188\n",
      "98.8401192507\n",
      "26.4083062745\n",
      "0.5\n"
     ]
    }
   ],
   "source": [
    "print(grid.at_node['erosion'].max())\n",
    "print(grid.at_node['utide'].max())\n",
    "print(grid.at_node['tauC'].max())\n",
    "print(grid.at_node['roughness_node'].max())\n",
    "print(grid.at_node['water_depth_at_node'].max())\n",
    "t = grid.at_node['tauC']\n",
    "tcr = grid.at_node['tau_cr_node']\n",
    "tt = t/tcr\n",
    "print(tt.max())\n",
    "print(t.max())\n",
    "print(tcr.max())\n",
    "\n",
    "#so no idea why tauC is so high - in Giulio it's 4.3, for us it's 93\n",
    "#also his max taucr is .5, ours is 199.99\n",
    "#our high tauC drives a high erosion\n",
    "#utide is similar range to his U (but note his does not have neg. values)\n",
    "#I've removed his for-loop (which does decr. erosion a bit in his code) and\n",
    "#I'm comparing his numbers sans for loop to our numbers (to make as close as possible)\n",
    "#his max erosion is 7.9!!! hours is 160...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ero mean: 0.000191468166903\n",
      "ero max: 0.0751540776334\n",
      "z min: -2.609347\n",
      "z min post erosion: -2.61419656315\n",
      "ero mean: 0.00018664713904\n",
      "ero max: 0.0777407265342\n",
      "z min: -2.61419656315\n",
      "z min post erosion: -2.61879734499\n",
      "ero mean: 0.000182115536497\n",
      "ero max: 0.0798310056436\n",
      "z min: -2.61879734499\n",
      "z min post erosion: -2.62316455658\n",
      "ero mean: 0.000177818366536\n",
      "ero max: 0.0814063030244\n",
      "z min: -2.62316455658\n",
      "z min post erosion: -2.62731102995\n",
      "ero mean: 0.000173720441274\n",
      "ero max: 0.0824763254786\n",
      "z min: -2.62731102995\n",
      "z min post erosion: -2.63124758125\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    ero = tec.totalsedimenterosion_mudsine(grid, mud_erodability, tidal_range, tcrgradeint)\n",
    "    #ero *= tidal_period/2 * 1/1025 #calc erosion over half the tidal cycle converting\n",
    "    ero *= 1/1025\n",
    "    print('ero mean: ' + str(ero.mean()))\n",
    "    print('ero max: ' +str(ero.max()))\n",
    "    print('z min: ' +str(z.min()))\n",
    "    z = grid.at_node['topographic__elevation']\n",
    "    z -= ero #update bed elevation\n",
    "    print('z min post erosion: ' +str(z.min()))\n",
    "    tfc.run_one_step()\n",
    "    tec.updategrids(grid,tfc)  \n",
    "    \n",
    "    #plt.figure()\n",
    "    #imshow_grid(grid,grid.at_node['topographic__elevation'])\n",
    "    #plt.title(str(i))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tec.plot_tidal_flow(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Distance (m)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "imshow_grid(grid, grid.at_node['mean_water__depth'], cmap='viridis', color_for_closed='g',vmax=3)\n",
    "plt.title('Water depth (m)')\n",
    "plt.xlabel('Distance (m)')\n",
    "plt.ylabel('Distance (m)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.32938522767\n"
     ]
    }
   ],
   "source": [
    "print(grid.at_node['flood_tide_flow__velocity_node'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.63124758125\n",
      "0.01\n",
      "0.134758275698\n"
     ]
    }
   ],
   "source": [
    "print(grid.at_node['mean_water__depth'].max())\n",
    "print(grid.at_node['mean_water__depth'].min())\n",
    "print(grid.at_node['mean_water__depth'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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